code: def train_and_save_model():
      # Load mnist dataset
      mnist = tf.keras.datasets.mnist
      (x_train, y_train), (x_test, y_test) = mnist.load_data()
      x_train, x_test = x_train / 255.0, x_test / 255.0
  
      # Train a simple neural net model
      model = tf.keras.models.Sequential(
          [
              tf.keras.layers.Flatten(input_shape=(28, 28)),
              tf.keras.layers.Dense(128, activation="relu"),
              tf.keras.layers.Dropout(0.2),
              tf.keras.layers.Dense(10),
          ]
      )
      loss_fn = tf.keras.losses.SparseCategoricalCrossentropy(
          from_logits=True
      )
      model.compile(
          optimizer="adam", loss=loss_fn, metrics=["accuracy"]
      )
      model.fit(x_train, y_train, epochs=1)
  
      model.evaluate(x_test, y_test, verbose=2)
      model.summary()
  
      # Save the model in h5 format in local file system
      model.save(TRAINED_MODEL_PATH)
  
file_path: ray/doc/source/serve/doc_code/tutorial_tensorflow.py
imports: from ray import serve
  import os
  import tempfile
  import numpy
  from starlette.requests import Request
  from typing import Dict
  import tensorflow
label_desc: Use image node_id 1 for calling a global function with example usage: train_and_save_model() without return types